TY - JOUR

T1 - Precision Indoor Three-Dimensional Visible Light Positioning Using Receiver Diversity and Multilayer Perceptron Neural Network

AU - Mahmoud, Abdulrahman Abdullahi

AU - Ahmad, Zahir

AU - Haas, Olivier

AU - Rajbhandari, Sujan

PY - 2020/12/1

Y1 - 2020/12/1

N2 - In recent times, several applications requiring highly accurate indoor positioning systems have been developed. Since the global positioning system is unavailable/less accurate in the indoor environment, alternative techniques such as visible light positioning (VLP) are considered. The VLP system benefits from the wide availability of illumination infrastructure, energy efficiency and the absence of electromagnetic interference. However, there is a limited number of studies on three dimensional (3D) VLP and the effect of multipath propagation on the accuracy of the 3D VLP. This study proposes a supervised artificial neural network to provide accurate 3D VLP whilst considering multipath propagation using receiver diversity. The results show that the proposed system can accurately estimate the 3D position with an average root mean square (RMS) error of 0.0198 and 0.021 m for line-of-sight (LOS) and non-LOS link, respectively. For 2D localisation, the average RMS errors are0.0103 and 0.0133 m, respectively.

AB - In recent times, several applications requiring highly accurate indoor positioning systems have been developed. Since the global positioning system is unavailable/less accurate in the indoor environment, alternative techniques such as visible light positioning (VLP) are considered. The VLP system benefits from the wide availability of illumination infrastructure, energy efficiency and the absence of electromagnetic interference. However, there is a limited number of studies on three dimensional (3D) VLP and the effect of multipath propagation on the accuracy of the 3D VLP. This study proposes a supervised artificial neural network to provide accurate 3D VLP whilst considering multipath propagation using receiver diversity. The results show that the proposed system can accurately estimate the 3D position with an average root mean square (RMS) error of 0.0198 and 0.021 m for line-of-sight (LOS) and non-LOS link, respectively. For 2D localisation, the average RMS errors are0.0103 and 0.0133 m, respectively.

U2 - 10.1049/iet-opt.2020.0046

DO - 10.1049/iet-opt.2020.0046

M3 - Article

VL - 14

SP - 440

EP - 446

JO - IET Optoelectronics

JF - IET Optoelectronics

SN - 1751-8768

IS - 6

ER -